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| Extraction of Effective Signal in Non-Invasive Blood Glucose Sensing with Near-Infrared Spectroscopy |
| HAN Guang, LIU Rong*, XU Ke-xin |
| State Key Laboratory of Precision Measuring Technology and Instruments，Tianjin University，Tianjin 300072，China |
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Abstract Relative measurement of near-infrared (NIR) spectroscopy is of great significance for the high precision detection of human blood glucose concentration in vivo. Although the similar background subtraction and double-beam design are frequently used in the in vitro experiments, they are not suitable for the human body detection because of the complex background variations. Reference measurement based on position is one of the most promising methods to realize in vivo reference measurement. The author proposes differential floating reference measurement method for in vivo reference measurement. The differential floating reference measurement is a universal method. However, it is difficult to determine the radial detection positions and extract the effective information from differential signals in practical use. To solve these problems, differential floating reference based on NAS (Net Analyte Signal)-VIP (Variable Importance in Projection)-SPXY (Sample set Partitioning based on joint X-Y distances)-PLS (Partial Least Square) method is proposed and the feasibility is investigated byin vitro and in vivo experiments, the results shows that the prediction accuracy and precision are improved through this method.
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Received: 2017-04-17
Accepted: 2017-09-25
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Corresponding Authors:
LIU Rong
E-mail: rongliu@tju.edu.cn
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[1] Yadav J, Rani A, Singh V, et al. Biomedical Signal Processing and Control, 2015, 18: 214.
[2] Amaral C E F, Wolf B. Medical Engineering & Physics, 2008, 30(5): 541.
[3] Min X L, Liu R, Hu Y X, et al. Analytical Methods, 2014, 6(24): 9831.
[4] Min X L, Liu R, Xu K X, et al. Optics & Laser Technology, 2017, 91: 7.
[5] Han G, Yu X Y, Xu K X, et al. Applied Spectroscopy, 2017, 71(9): 2177.
[6] Du Y P, Liang Y Z, Kasemsumran S, et al. Analytical Sciences, 2004, 20(9): 1339.
[7] LI Qing-bo, HUANG Zheng-wei(李庆波，黄政伟). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2014，34(2): 494.
[8] Palermo R N, Short S M, Anderson C A, et al. Journal of Pharmaceutical Innovation, 2012, 7(2): 56.
[9] Zou X B, Zhao J W, Malcolm J W P, et al. Analytica Chimica Acta, 2010, 667(1-2): 14.
[10] Gani W, Limam M. Journal of Statistical Computation & Simulation, 2016, 86(1): 135.
[11] Clarke W L. Diabetes Technol. Ther., 2005, 7(5): 776. |
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